Dataset Analysis

Dataset Diversity Analysis

2025-12-07 15:07:39 52002 conversations 156006 messages

Overview

Dataset
tatsu-lab/alpaca
Conversations
52002
Analyzed
52002
Coverage
100.0%
Messages
156006
Analyzers
length, diversity, question_diversity

Recommendations

13 issues
medium Outliers detected in diversity vocabulary richness
Found 1612 samples (1.0%) with values outside 3.0 standard deviations from the mean. High outliers: 1612, Low outliers: 0. Consider reviewing these samples for potential data quality issues.
1612 samples text_content_diversity_vocabulary_richness
medium Inconsistent instruction formatting detected
Found multiple instruction format patterns in the dataset: alpaca: 20679, vicuna: 34. Mixing formats may confuse the model and reduce training effectiveness. Consider standardizing to a single format.
20713 samples text_content
low Multimodal distribution in length word count
Detected 4 distinct modes in the distribution (confidence: 43%). Mode 1: 44369 samples (31.3%), mean=7.83, std=3.03; Mode 4: 84998 samples (50.1%), mean=19.32, std=4.68; Mode 2: 24750 samples (16.6%), mean=69.47, std=24.14; Mode 3: 1889 samples (2.0%), mean=187.98, std=59.21. This may indicate different types of content (e.g., short questions vs long explanations). Outlier detection uses per-mode statistics to avoid false positives.
156006 samples text_content_length_word_count
low Outliers detected in length word count
Found 478 samples (0.3%) that are outliers within their respective modes. Distribution has 4 modes (mode 0: μ=7.83, σ=3.03, mode 3: μ=19.32, σ=4.68, mode 1: μ=69.47, σ=24.14, mode 2: μ=187.98, σ=59.21). Outliers are samples more than 3.0 std from mode mean.
478 samples text_content_length_word_count
low Multimodal distribution in length token count
Detected 5 distinct modes in the distribution (confidence: 57%). Mode 1: 92322 samples (55.9%), mean=14.33, std=5.04; Mode 3: 34284 samples (24.4%), mean=28.0, std=2.94; Mode 4: 23431 samples (14.5%), mean=70.98, std=21.11; Mode 2: 4657 samples (3.9%), mean=138.86, std=19.54; Mode 5: 1312 samples (1.2%), mean=257.67, std=77.4. This may indicate different types of content (e.g., short questions vs long explanations). Outlier detection uses per-mode statistics to avoid false positives.
156006 samples text_content_length_token_count
low Outliers detected in length token count
Found 1135 samples (0.7%) that are outliers within their respective modes. Distribution has 5 modes (mode 0: μ=14.33, σ=5.04, mode 2: μ=28.0, σ=2.94, mode 3: μ=70.98, σ=21.11, mode 1: μ=138.86, σ=19.54, mode 4: μ=257.67, σ=77.4). Outliers are samples more than 3.0 std from mode mean.
1135 samples text_content_length_token_count
low Outliers detected in diversity unique words ratio
Found 1211 samples (0.8%) with values outside 3.0 standard deviations from the mean. High outliers: 0, Low outliers: 1211. Consider reviewing these samples for potential data quality issues.
1211 samples text_content_diversity_unique_words_ratio
low Outliers detected in diversity type token ratio
Found 1211 samples (0.8%) with values outside 3.0 standard deviations from the mean. High outliers: 0, Low outliers: 1211. Consider reviewing these samples for potential data quality issues.
1211 samples text_content_diversity_type_token_ratio
low Outliers detected in diversity hapax legomena ratio
Found 1115 samples (0.7%) with values outside 3.0 standard deviations from the mean. High outliers: 0, Low outliers: 1115. Consider reviewing these samples for potential data quality issues.
1115 samples text_content_diversity_hapax_legomena_ratio
low Outliers detected in question diversity cluster id
Found 7 samples (0.0%) with values outside 3.0 standard deviations from the mean. High outliers: 7, Low outliers: 0. Consider reviewing these samples for potential data quality issues.
7 samples text_content_question_diversity_cluster_id
low Outliers detected in question diversity cluster size
Found 7 samples (0.0%) with values outside 3.0 standard deviations from the mean. High outliers: 0, Low outliers: 7. Consider reviewing these samples for potential data quality issues.
7 samples text_content_question_diversity_cluster_size
low Empty or near-empty messages detected
Found 1188 messages (0.8%) with 5 or fewer characters. These may indicate data quality issues or placeholder content that should be reviewed.
1188 samples text_content
low Many short messages detected
Found 29201 messages (18.7%) with fewer than 10 words. This may be intentional (e.g., short responses) or indicate low-quality samples worth reviewing.
29201 samples text_content_length_word_count

Distributions

9 charts

Length Word Count (4 modes)

multimodal 4 distinct modes detected
Mode 1 31.3%
Mean 7.8
Std 3.0
Count 44369
Mode 4 50.1%
Mean 19.3
Std 4.7
Count 84998
Mode 2 16.6%
Mean 69.5
Std 24.1
Count 24750
Mode 3 2.0%
Mean 188.0
Std 59.2
Count 1889

Length Token Count (5 modes)

multimodal 5 distinct modes detected
Mode 1 55.9%
Mean 14.3
Std 5.0
Count 92322
Mode 3 24.4%
Mean 28.0
Std 2.9
Count 34284
Mode 4 14.5%
Mean 71.0
Std 21.1
Count 23431
Mode 2 3.9%
Mean 138.9
Std 19.5
Count 4657
Mode 5 1.2%
Mean 257.7
Std 77.4
Count 1312

Diversity Unique Words Ratio

Diversity Type Token Ratio

Diversity Vocabulary Richness

Diversity Hapax Legomena Ratio

Question Diversity Cluster Id

Question Diversity Cluster Size

Role Distribution

Anomaly Detection

5 visualizations

Outliers in Length Word Count

3119 outliers

Outliers in Length Token Count

3050 outliers

Outliers in Diversity Unique Words Ratio

1434 outliers

Outliers in Diversity Type Token Ratio

1434 outliers

Outliers in Diversity Vocabulary Richness

1649 outliers

Clustering Analysis

Question Diversity Clusters (click to view samples)

Total Clusters
3
Questions Analyzed
52002
Noise Samples
51995
Noise 51995 samples 100.0%
user 0
Give three tips for staying healthy.
user 1
What are the three primary colors?
user 2
Describe the structure of an atom.
user 3
How can we reduce air pollution?
user 4
Describe a time when you had to make a difficult decision.
+ 51990 more samples in this cluster
Cluster 0 2 samples 0.0%
user 1426
Explain the difference between artificial intelligence and machine learning
user 20430
Explain the difference between Machine Learning and Artificial Intelligence.
Cluster 1 2 samples 0.0%
user 19732
Describe the difference between aerobic and anaerobic exercise.
user 27974
Describe the differences between anaerobic and aerobic exercise.
Cluster 2 3 samples 0.0%
user 46064
Cite which sources were used in the paper ### Input: Abstract: Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the feasibility of extracting a discrete (textual) interpretation of continuous prompts that is faithful to the problem they solve. In practice, we observe a "wayward" behavior between the task solved by continuous prompts and their nearest neighbor discrete projections: We can find continuous prompts that solve a task while being projected to an arbitrary text (e.g., definition of a different or even a contradictory task), while being within a very small (2%) margin of the best continuous prompt of the same size for the task. We provide intuitions behind this odd and surprising behavior, as well as extensive empirical analyses quantifying the effect of various parameters. For instance, for larger model sizes we observe higher waywardness, i.e, we can find prompts that more closely map to any arbitrary text with a smaller drop in accuracy. These findings have important implications relating to the difficulty of faithfully interpreting continuous prompts and their generalization across models and tasks, providing guidance for future progress in prompting language models.
user 46065
Classify the abstract under a label. ### Input: Abstract: Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the feasibility of extracting a discrete (textual) interpretation of continuous prompts that is faithful to the problem they solve. In practice, we observe a "wayward" behavior between the task solved by continuous prompts and their nearest neighbor discrete projections: We can find continuous prompts that solve a task while being projected to an arbitrary text (e.g., definition of a different or even a contradictory task), while being within a very small (2%) margin of the best continuous prompt of the same size for the task. We provide intuitions behind this odd and surprising behavior, as well as extensive empirical analyses quantifying the effect of various parameters. For instance, for larger model sizes we observe higher waywardness, i.e, we can find prompts that more closely map to any arbitrary text with a smaller drop in accuracy. These findings have important implications relating to the difficulty of faithfully interpreting continuous prompts and their generalization across models and tasks, providing guidance for future progress in prompting language models.
user 46070
State the main arguments that the abstract makes ### Input: Abstract: Fine-tuning continuous prompts for target tasks has recently emerged as a compact alternative to full model fine-tuning. Motivated by these promising results, we investigate the feasibility of extracting a discrete (textual) interpretation of continuous prompts that is faithful to the problem they solve. In practice, we observe a "wayward" behavior between the task solved by continuous prompts and their nearest neighbor discrete projections: We can find continuous prompts that solve a task while being projected to an arbitrary text (e.g., definition of a different or even a contradictory task), while being within a very small (2%) margin of the best continuous prompt of the same size for the task. We provide intuitions behind this odd and surprising behavior, as well as extensive empirical analyses quantifying the effect of various parameters. For instance, for larger model sizes we observe higher waywardness, i.e, we can find prompts that more closely map to any arbitrary text with a smaller drop in accuracy. These findings have important implications relating to the difficulty of faithfully interpreting continuous prompts and their generalization across models and tasks, providing guidance for future progress in prompting language models.

Message Statistics

Metric Distribution Mean Std Min Max Median
text_content_word_count multimodal (4) 26.05 29.75 0.0 717.0 16.0
└ Mode 1 (31.3%) 44369 samples 7.83 3.03 - - -
└ Mode 4 (50.1%) 84998 samples 19.32 4.68 - - -
└ Mode 2 (16.6%) 24750 samples 69.47 24.14 - - -
└ Mode 3 (2.0%) 1889 samples 187.98 59.21 - - -
text_content_token_count multimodal (5) 31.61 36.48 0.0 958.0 18.0
└ Mode 1 (55.9%) 92322 samples 14.33 5.04 - - -
└ Mode 3 (24.4%) 34284 samples 28.0 2.94 - - -
└ Mode 4 (14.5%) 23431 samples 70.98 21.11 - - -
└ Mode 2 (3.9%) 4657 samples 138.86 19.54 - - -
└ Mode 5 (1.2%) 1312 samples 257.67 77.4 - - -
text_content_words_ratio unimodal 0.88 0.11 0.0 1.0 0.88
text_content_token_ratio unimodal 0.88 0.11 0.0 1.0 0.88
text_content_vocabulary_richness unimodal 3.87 1.29 0.0 14.95 3.5
text_content_legomena_ratio unimodal 0.89 0.09 0.0 1.0 0.86
text_content_cluster_id unimodal -1.0 0.03 -1.0 2.0 -1.0
text_content_cluster_size unimodal 51988.0 603.19 2.0 51995.0 51995.0

Conversation Turns

Statistic Value
Count52002
Mean3.0
Std0.0
Min3
Max3
Median3.0

Affected Conversations